As the environment of special targets is complex and constantly changing, better requirements are put forward for rapid and high-precision target detection and recognition. In this paper, the improved YOLOV7 algorithm is adopted. First, Kmeans is used to match the new anchor coordinates, and multiple detection scales are added to improve the detection accuracy; Secondly, the attention mechanism module is integrated into the feature extraction network Darknet-53 to obtain important features; Then, taking advantage of the lightweight technology of Ghost module, Ghost BottleNeck composed of Ghost modules is introduced to replace the Neck module in YOLOV7, which greatly reduces the parameters and computation of the network model; Finally, IOU_ Nms is modified to DIOU_ Nms is used to optimize the loss function. experiments show that the accuracy and real-time performance of the algorithm are improved.
In order to strike the target with high precision, the beam pointing accuracy of laser weapon needs to reach μrad level. The hysteresis, creep and high frequency non-linearity of piezoelectric ceramics (PZT) are the main factors that affect the system's pointing accuracy. In order to improve the dynamic control accuracy of fast steering mirror (FSM), the motion principle of FSM is introduced. The electromechanical model and the mathematical model of control object are proposed. The Smith predictive control algorithm is used to design control parameters and to validate the control algorithm by simulation.
In the international environment of violence, terrorist attacks and illegal drug smuggling, security issues are particularly important. Many companies and institutions in the world have successively developed safety inspection equipment, among which x-ray safety inspection equipment has been widely used. In order to ensure the safety of passengers, it is necessary to be able to accurately identify the knives, guns, inflammables, explosives and other dangerous articles in the luggage package during the security check, so as to reduce the probability of danger. At present, airport security inspectors need to change for half an hour, which is hard to work and easy to miss. An assistant identification system of contraband based on yolov4 to assist the security inspector to judge the X-ray image was proposed to improve the efficiency and accuracy of security inspection identification, reduce the manual detection intervention of professional training personnel, and avoid the occurrence of missed detection and false detection. A novel data augmentation method is proposed to guarantee the performance of the system in the case of a small number of samples. The experimental results show that the system has strong robustness in different channel directions and complex scenes, the comprehensive detection rate of lighter is higher than 95%, and the recognition efficiency and accuracy are greatly improved compared with the traditional convolutional neural network(CNN).
In order to solve the problems of serious electromagnetic interference, high cost and long detection time of radaroptoelectronic traditional airport runways Foreign Objects Debris Detection(FOD) system, a novel FOD detection algorithm was proposed which based on the improved yolov3. The multi-scale detection and feature extraction network were used to improve the learning ability of object features. Meanwhile, the classical image processing and the deep learning technology was adopt, the algorithm has the ability of target autonomous recognition, besides conventional image restoration. The experimental results show that the algorithm has a strong ability of autonomous recognition and environmental adaptability, the time consumption is better than 0.2s, which can meet the real-time detection of foreign objects debris. It has a guiding significance for the next stage of the engineering of pure-photoelectric foreign object detection system.
In view of the problem of projection distortion in the result of aberration-free point test of off-axis paraboloid mirror, this paper analyzes its formation principle, puts forward a transformation method from the detection coordinate system to the processing coordinate system, according to the mathematical relationship between the two, reconstructs the surface shape of the detection result, realizes the transformation of projection distortion image, and analyzes the error of the transformation result by using the fucial function. It is proved that this method is feasible by using the reconstructed surface results to guide the NC Polishing.
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